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\n",
                "text/plain": "<Figure size 640x480 with 1 Axes>"
              }
            ],
            "_view_module": "@jupyter-widgets/output",
            "_model_module_version": "1.0.0",
            "_view_count": null,
            "_view_module_version": "1.0.0",
            "layout": "IPY_MODEL_5f5ec03b5c7042ebbbaaba68fb73c3af",
            "_model_module": "@jupyter-widgets/output"
          }
        },
        "5f5ec03b5c7042ebbbaaba68fb73c3af": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "state": {
            "_view_name": "LayoutView",
            "grid_template_rows": null,
            "right": null,
            "justify_content": null,
            "_view_module": "@jupyter-widgets/base",
            "overflow": null,
            "_model_module_version": "1.2.0",
            "_view_count": null,
            "flex_flow": null,
            "width": null,
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  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Divyanshu-ISM/Oil-and-Gas-data-analysis/blob/master/PYPDERS2D.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8wmamAQfCNDk",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 547
        },
        "outputId": "17335921-1fd5-4985-8b67-3e9fde9be4b9"
      },
      "source": [
        "!pip install py-pde\n",
        "\n",
        "!pip install h5py pandas pyfftw tqdm"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting py-pde\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/f2/2a/45a24610e33c92f07b52c036c196890228f6dc87232bf8d34de64b163ce2/py_pde-0.9.4-py3-none-any.whl (278kB)\n",
            "\r\u001b[K     |█▏                              | 10kB 8.2MB/s eta 0:00:01\r\u001b[K     |██▍                             | 20kB 1.8MB/s eta 0:00:01\r\u001b[K     |███▌                            | 30kB 2.3MB/s eta 0:00:01\r\u001b[K     |████▊                           | 40kB 2.5MB/s eta 0:00:01\r\u001b[K     |█████▉                          | 51kB 2.0MB/s eta 0:00:01\r\u001b[K     |███████                         | 61kB 2.3MB/s eta 0:00:01\r\u001b[K     |████████▎                       | 71kB 2.5MB/s eta 0:00:01\r\u001b[K     |█████████▍                      | 81kB 2.7MB/s eta 0:00:01\r\u001b[K     |██████████▋                     | 92kB 3.0MB/s eta 0:00:01\r\u001b[K     |███████████▊                    | 102kB 2.8MB/s eta 0:00:01\r\u001b[K     |█████████████                   | 112kB 2.8MB/s eta 0:00:01\r\u001b[K     |██████████████▏                 | 122kB 2.8MB/s eta 0:00:01\r\u001b[K     |███████████████▎                | 133kB 2.8MB/s eta 0:00:01\r\u001b[K     |████████████████▌               | 143kB 2.8MB/s eta 0:00:01\r\u001b[K     |█████████████████▋              | 153kB 2.8MB/s eta 0:00:01\r\u001b[K     |██████████████████▉             | 163kB 2.8MB/s eta 0:00:01\r\u001b[K     |████████████████████            | 174kB 2.8MB/s eta 0:00:01\r\u001b[K     |█████████████████████▏          | 184kB 2.8MB/s eta 0:00:01\r\u001b[K     |██████████████████████▍         | 194kB 2.8MB/s eta 0:00:01\r\u001b[K     |███████████████████████▌        | 204kB 2.8MB/s eta 0:00:01\r\u001b[K     |████████████████████████▊       | 215kB 2.8MB/s eta 0:00:01\r\u001b[K     |██████████████████████████      | 225kB 2.8MB/s eta 0:00:01\r\u001b[K     |███████████████████████████     | 235kB 2.8MB/s eta 0:00:01\r\u001b[K     |████████████████████████████▎   | 245kB 2.8MB/s eta 0:00:01\r\u001b[K     |█████████████████████████████▍  | 256kB 2.8MB/s eta 0:00:01\r\u001b[K     |██████████████████████████████▋ | 266kB 2.8MB/s eta 0:00:01\r\u001b[K     |███████████████████████████████▉| 276kB 2.8MB/s eta 0:00:01\r\u001b[K     |████████████████████████████████| 286kB 2.8MB/s \n",
            "\u001b[?25hRequirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (from py-pde) (1.18.5)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.6/dist-packages (from py-pde) (1.4.1)\n",
            "Requirement already satisfied: numba in /usr/local/lib/python3.6/dist-packages (from py-pde) (0.48.0)\n",
            "Requirement already satisfied: sympy in /usr/local/lib/python3.6/dist-packages (from py-pde) (1.1.1)\n",
            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.6/dist-packages (from py-pde) (3.2.2)\n",
            "Requirement already satisfied: llvmlite<0.32.0,>=0.31.0dev0 in /usr/local/lib/python3.6/dist-packages (from numba->py-pde) (0.31.0)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.6/dist-packages (from numba->py-pde) (49.6.0)\n",
            "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.6/dist-packages (from sympy->py-pde) (1.1.0)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.6/dist-packages (from matplotlib->py-pde) (0.10.0)\n",
            "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib->py-pde) (2.8.1)\n",
            "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib->py-pde) (2.4.7)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib->py-pde) (1.2.0)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from cycler>=0.10->matplotlib->py-pde) (1.15.0)\n",
            "Installing collected packages: py-pde\n",
            "Successfully installed py-pde-0.9.4\n",
            "Requirement already satisfied: h5py in /usr/local/lib/python3.6/dist-packages (2.10.0)\n",
            "Requirement already satisfied: pandas in /usr/local/lib/python3.6/dist-packages (1.0.5)\n",
            "Collecting pyfftw\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/dc/77/2a039e2c411b6532ae5b8e5e922d31fdb6140246f92379d9a1dce4872a20/pyFFTW-0.12.0-cp36-cp36m-manylinux1_x86_64.whl (2.6MB)\n",
            "\u001b[K     |████████████████████████████████| 2.6MB 2.8MB/s \n",
            "\u001b[?25hRequirement already satisfied: tqdm in /usr/local/lib/python3.6/dist-packages (4.41.1)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from h5py) (1.15.0)\n",
            "Requirement already satisfied: numpy>=1.7 in /usr/local/lib/python3.6/dist-packages (from h5py) (1.18.5)\n",
            "Requirement already satisfied: python-dateutil>=2.6.1 in /usr/local/lib/python3.6/dist-packages (from pandas) (2.8.1)\n",
            "Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.6/dist-packages (from pandas) (2018.9)\n",
            "Installing collected packages: pyfftw\n",
            "Successfully installed pyfftw-0.12.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wgTlbVJGCePD",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import scipy as sc\n",
        "import numba as nu\n",
        "import sympy as sym"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rusaX-Gla6Rx",
        "colab_type": "text"
      },
      "source": [
        "#Simple Diffusivity Equation in 2D"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "enbEBK9zCiAM",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from pde import DiffusionPDE, ScalarField, UnitGrid"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x4XPmnIPccHR",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "0eaa8924-86e9-4adb-98e3-4329d5cea7ac"
      },
      "source": [
        "plt.style.use('default')\n",
        "plt.figure(figsize=(10,6))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 0 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 55
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 0 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ydmxHcNFCmgF",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 521,
          "referenced_widgets": [
            "dfe80c1d368f431d88498ade4fdb19d8",
            "c27a2d32e5654ab3a256ac2e6b1df3bd",
            "c167b00e492944b999a0d6924611a0d3",
            "6206591be9144fb3b3c4d320357155dc",
            "08e1b5d0cefb41f89bdde75b3e66b021",
            "221971cb45124c09b74ea373b9ccc4dd",
            "7a6e10d4313f45c18dd14a1410b67098",
            "825cbcc47cf642799b0afde73c086881"
          ]
        },
        "outputId": "fa4e520d-4400-40b6-ea00-c6b850d1e361"
      },
      "source": [
        "#1. Generate a Grid \n",
        "grid = UnitGrid([100,100])\n",
        "\n",
        "#2. Give the Initial Condition to the grid\n",
        "#Here We're creating a Field With Uniformly distributed Values (Let's say of Porosity)\n",
        "i_state = ScalarField.random_uniform(grid=grid,vmin=0.10,vmax=0.40,label='Porosity')\n",
        "\n",
        "#3. Now Let's Define the PDE\n",
        "eq = DiffusionPDE(diffusivity=0.1)  # define the pde\n",
        "\n",
        "#4. Results\n",
        "result = eq.solve(i_state, t_range=100)\n",
        "\n",
        "#5. Plot Results\n",
        "result.plot()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "dfe80c1d368f431d88498ade4fdb19d8",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<pde.tools.plotting.PlotReference at 0x7f2d25260088>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4vhKX0F83ewi",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pde\n",
        "grid = pde.UnitGrid([100, 100])"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WBdQs6933xL8",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "b21f5cb9-2c1e-49ae-93b6-e438e59d7359"
      },
      "source": [
        "plt.style.use('default')\n",
        "plt.figure(figsize=(10,10))\n",
        "grid = pde.UnitGrid([100, 100])\n",
        "grid"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "UnitGrid(shape=(100, 100), periodic=[False, False])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x1000 with 0 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3QXWrcFE39xI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "Pi = np.ones(grid.shape)*5000\n",
        "Pi[20:30,60:70] = 100\n",
        "Pi[60:70,60:70] = 1000\n",
        "\n",
        "Pi[20:30,20:30] = 100\n",
        "Pi[60:70,20:30] = 1000"
      ],
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Yt1lY89rCokc",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
          "referenced_widgets": [
            "ddf914b949cb49e0a2f052551ad62bd4",
            "890b4a43827644149c65dafe69509fde",
            "4d7178b7da0945d9a8dc836d7d0b1218",
            "0c5df27864294f1983eff4a945840d39",
            "b2f002cef3ef4247aa75f6024ebcc36d",
            "37469c26929647bd920e928a12bc1a9c",
            "7bb2e74c6aa44e308e67603a5812995f",
            "11ee4e42919e48bcbc65c3b5c29d9e71",
            "dc1675fe41164b8fb27f1be7407ea26c",
            "ddde837452b342cfa3321a002556fc41"
          ]
        },
        "cellView": "code",
        "outputId": "8a5fad3c-8bcf-45d6-aad7-f9ab3ce9a4a7"
      },
      "source": [
        "#@title Default title text\n",
        "import pde\n",
        "\n",
        " # generate grid\n",
        "state = pde.ScalarField(grid=grid,data=Pi)  # generate initial condition\n",
        "\n",
        "storage = pde.MemoryStorage()\n",
        "\n",
        "trackers = [\n",
        "    \"progress\",  # show progress bar during simulation\n",
        "    \"steady_state\",  # abort when steady state is reached\n",
        "    storage.tracker(interval=1),  # store data every simulation time unit\n",
        "    pde.PlotTracker(show=True,title=\"Interference in a High-(Kh/mu) Reservoir\"),  # show images during simulation\n",
        "    # print some output every 5 real seconds:\n",
        "    pde.PrintTracker(interval=pde.RealtimeIntervals(duration=100))\n",
        "]\n",
        "\n",
        "eq = pde.DiffusionPDE(1)  # define the PDE\n",
        "eq.solve(state, 100, dt=0.1, tracker=trackers)\n",
        "\n",
        "for field in storage:\n",
        "    print(field.integral)\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "# plt.colorbar()"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "ddf914b949cb49e0a2f052551ad62bd4",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "dc1675fe41164b8fb27f1be7407ea26c",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Output()"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "t=0, c=4.82e+03±877\n",
            "t=0.2, c=4.82e+03±821\n",
            "t=1.7, c=4.82e+03±688\n",
            "t=36, c=4.82e+03±221\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Spent more time on handling trackers (18.77126859199973) than on the actual simulation (1.1008868360002566)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "48220000.0\n",
            "48220000.0\n",
            "48220000.00000001\n",
            "48220000.0\n",
            "48220000.0\n",
            "48220000.0\n",
            "48220000.0\n",
            "48220000.0\n",
            "48220000.0\n",
            "48219999.99999999\n",
            "48220000.0\n",
            "48220000.0\n",
            "48220000.0\n",
            "48220000.0\n",
            "48220000.0\n",
            "48220000.0\n",
            "48220000.00000001\n",
            "48220000.0\n",
            "48220000.000000015\n",
            "48220000.0\n",
            "48219999.99999999\n",
            "48220000.0\n",
            "48220000.0\n",
            "48220000.0\n",
            "48220000.0\n",
            "48220000.0\n",
            "48220000.00000001\n",
            "48220000.0\n",
            "48220000.0\n",
            "48220000.0\n",
            "48220000.0\n",
            "48220000.0\n",
            "48220000.0\n",
            "48220000.00000001\n",
            "48220000.0\n",
            "48220000.0\n",
            "48220000.0\n",
            "48220000.0\n",
            "48220000.0\n",
            "48220000.00000001\n",
            "48220000.0\n",
            "48220000.0\n",
            "48220000.0\n",
            "48220000.0\n",
            "48220000.0\n",
            "48220000.0\n",
            "48220000.0\n",
            "48220000.0\n",
            "48220000.0\n",
            "48220000.0\n",
            "48219999.99999999\n",
            "48220000.0\n",
            "48220000.0\n",
            "48220000.0\n",
            "48220000.0\n",
            "48219999.99999999\n",
            "48220000.0\n",
            "48220000.0\n",
            "48220000.0\n",
            "48220000.0\n",
            "48220000.0\n",
            "48220000.0\n",
            "48220000.00000001\n",
            "48220000.0\n",
            "48220000.0\n",
            "48220000.0\n",
            "48220000.0\n",
            "48220000.0\n",
            "48220000.0\n",
            "48220000.0\n",
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            "48220000.0\n",
            "48220000.00000001\n",
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            "48220000.0\n",
            "48220000.00000001\n",
            "48220000.0\n",
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            "48220000.00000001\n",
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            "48220000.0\n",
            "48220000.0\n",
            "48220000.0\n",
            "48220000.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YU90mkkLdgo9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "Pi = np.ones(grid.shape)*5000\n",
        "Pi[15:20,15:20] = 100"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SBf3f7nvd4K4",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NFB-nl1ojDlo",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kho8bbXbjX41",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
          "referenced_widgets": [
            "c9b643d1f1d74467b7fb57dd4a43e578",
            "a041277726c44d2bbe85eb0bb29ab749",
            "8e54418a13e2434188f6092676ba9ea3",
            "608c39ea385444778f787c0bd5eb65c9",
            "80b463a3261b47419c3cdc929f966573",
            "71d68b2279bd4ace8095bb6ef303e652",
            "51e1f5ea7aaf4118867f046bddc723c1",
            "d7923a3be11f4cbd9bd486558dacbeb5",
            "691b5544b39d46639157e96ec8cc6a88",
            "5f5ec03b5c7042ebbbaaba68fb73c3af"
          ]
        },
        "outputId": "f3173d5a-277a-4116-f6fd-ba72c817f895"
      },
      "source": [
        "import pde\n",
        "\n",
        " # generate grid\n",
        "state = pde.ScalarField(grid=grid,data=Pi)  # generate initial condition\n",
        "\n",
        "storage = pde.MemoryStorage()\n",
        "\n",
        "trackers = [\n",
        "    \"progress\",  # show progress bar during simulation\n",
        "    \"steady_state\",  # abort when steady state is reached\n",
        "    storage.tracker(interval=1),  # store data every simulation time unit\n",
        "    pde.PlotTracker(show=True,title='Pressure Diffusion in a (Tight) Reservoir'),  # show images during simulation\n",
        "    # print some output every 5 real seconds:\n",
        "    pde.PrintTracker(interval=pde.RealtimeIntervals(duration=100))\n",
        "]\n",
        "\n",
        "eq = pde.DiffusionPDE(0.3)  # define the PDE\n",
        "eq.solve(state, 100, dt=0.1, tracker=trackers)\n",
        "\n",
        "for field in storage:\n",
        "    print(field.integral)\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "#"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "c9b643d1f1d74467b7fb57dd4a43e578",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "691b5544b39d46639157e96ec8cc6a88",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Output()"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "t=0, c=4.82e+03±877\n",
            "t=0.2, c=4.82e+03±857\n",
            "t=1.5, c=4.82e+03±786\n",
            "t=30.7, c=4.82e+03±451\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Spent more time on handling trackers (18.816123430000403) than on the actual simulation (1.010091898999633)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "48220000.0\n",
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            "48220000.0\n",
            "48220000.00000001\n",
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            "48220000.0\n",
            "48220000.00000001\n",
            "48220000.00000001\n",
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            "48220000.0\n",
            "48220000.0\n",
            "48220000.00000001\n",
            "48220000.00000001\n",
            "48220000.00000001\n",
            "48220000.0\n",
            "48220000.00000001\n",
            "48220000.0\n",
            "48220000.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jN7BgNII6gDR",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}